Maternal and child health data management system based on full life cycle

Abstract

The invention discloses a maternal and child health data management system based on a full life cycle, and relates to the technical field of data management, comprising the following steps: collecting and standardizing maternal and child health data to obtain health data; performing maternal and child health risk detection according to the health data to obtain health risk data; performing health early warning according to the health risk data to obtain early warning information; making intervention measures according to the early warning data to obtain the intervention measures; performing effect tracking according to the intervention measures to obtain an intervention result; performing health assessment according to the intervention result to obtain an assessment result; performing long-term health prediction according to the evaluation result to obtain a health prediction result; and performing personalized health management according to the health prediction result. According to the method, the overall effect of intervention measures on maternal and child health can be comprehensively and accurately evaluated, the accuracy of health management is improved, the comprehensiveness and reliability of prediction are enhanced, and the accuracy of long-term health trend prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a maternal and child health data management system based on the entire life cycle. Background Art

[0002] Maternal and child health management runs through the entire life cycle of women from adolescence, pregnancy to menopause, and involves the growth and development of newborns, infants and children. The current management of maternal and child health data still faces many challenges.

[0003] In the existing technology, health assessment has shortcomings: At present, health assessment mainly relies on static data, such as one-time health examinations, questionnaires or short-term follow-up records. Such methods are difficult to fully reflect the dynamic changes in individual health status and lack the ability to track changes in health status over time.

[0004] There are deficiencies in the existing technology for long-term health prediction: current long-term health prediction methods are mainly based on traditional statistical regression models, which make it difficult to fully utilize long-term follow-up data, fail to model the nonlinear characteristics of health status changing over time, and are often based on only a few key indicators, limiting the prediction accuracy. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a maternal and child health data management system based on the entire life cycle to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a maternal and child health data management system based on the entire life cycle, comprising the following steps: S1. Collection and standardization of maternal and child health data to obtain health data; S2. Perform maternal and child health risk detection based on health data to obtain health risk data; S3. Conduct health warning based on health risk data and obtain warning information; S4, formulate intervention measures based on early warning data and obtain intervention measures; S5. Follow up the effects of intervention measures and obtain the intervention results; S6. Conduct health assessment based on the intervention results and obtain the assessment results; S7. Perform long-term health prediction based on the evaluation results to obtain health prediction results; S8. Conduct personalized health management based on health prediction results.

[0007] To further optimize this technical solution, the maternal and child health risk detection in S2 includes: By using health risk detection models and combining maternal and child health data obtained from multiple sources, we can identify health risks, provide accurate health risk detection, and help women and children identify hidden risks in advance.

[0008] To further optimize the technical solution, the health risk detection model includes: ; in: : Risk prediction value; : Neural network activation function; : Weight parameter of the i-th category physiological data; : The i-th type of physiological data at time point t; : Weight parameter of the j-th type of environmental data; : The j-th type of environmental data at time point t; : Bias term.

[0009] To further optimize this technical solution, the health warning in S3 includes: Based on health risk data, clinical experience and medical research data, high-risk factors in maternal and child health data are identified, and health warnings are provided to maternal and child personnel and medical institutions.

[0010] To further optimize the technical solution, the health assessment in S6 includes: Based on the intervention results, a health assessment model is used, combined with in-depth analysis of multidimensional data, to evaluate the overall effect of the intervention measures on maternal and child health.

[0011] To further optimize this technical solution, the health assessment model includes: ; in: : Health status assessment value at time point t; , , : Weight coefficients of physiological data, environmental data and psychosocial data; : The change of the physiological data of the i-th category; : The change in the jth type of environmental data; : weight coefficient of the kth category of psychosocial data; : The amount of change in the kth category of psychosocial data.

[0012] To further optimize the technical solution, the long-term health prediction in S7 includes: Based on the health assessment results, a health prediction model is used to combine historical data and individualized health records, and consider the long-term impact of multiple factors to predict the individual's future health status.

[0013] To further optimize the technical solution, the health prediction model includes: ; in: : The long-term health prediction value at time point t+1; : Current health value; : feedback coefficient; : Feedback coefficient of the i-th type of physiological data; : Feedback coefficient of the jth type of environmental data; : Feedback coefficient for the kth category of psychosocial data.

[0014] To further optimize the technical solution, the personalized health management in S8 includes: Based on different health prediction results, personalized health management plans are formulated for women and children, and the content and cycle of the health management plans are flexibly adjusted according to the prediction results.

[0015] Further optimize this technical solution, including the following functional modules: Data processing module, risk detection module, health warning module, health intervention module, data monitoring module, health assessment module, health prediction module, and health management module.

[0016] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a maternal and child health data management system based on the entire life cycle as described in the first aspect of the present invention are implemented.

[0017] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a maternal and child health data management system based on the entire life cycle as described in the first aspect of the present invention are implemented.

[0018] Compared with the prior art, the present invention provides a maternal and child health data management system based on the entire life cycle, which has the following beneficial effects: This maternal and child health data management system based on the entire life cycle, through the health assessment model, calculates the degree of individual health improvement based on multi-dimensional data fusion. It can comprehensively and accurately evaluate the overall effect of intervention measures on maternal and child health, and construct a health assessment curve that changes over time, monitor individual health trends in real time, and improve the accuracy of health management.

[0019] Through the long-term health prediction model, a variety of health data are incorporated into the prediction model, which enhances the comprehensiveness and reliability of the prediction. In addition, a dynamic prediction model is constructed by combining the changing characteristics of health status over time, which improves the accuracy of long-term health trend predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A schematic diagram of a process flow of a maternal and child health data management system based on the entire life cycle proposed by the present invention; Figure 2 A flow chart of a health risk detection model for a maternal and child health data management system based on the entire life cycle proposed by the present invention; Figure 3 A flow chart of a health assessment model for a maternal and child health data management system based on the entire life cycle proposed by the present invention; Figure 4 A flow chart of a long-term health prediction model for a maternal and child health data management system based on the whole life cycle proposed by the present invention; Figure 5 This is a module schematic diagram of a maternal and child health data management system based on the entire life cycle proposed by the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0025] Embodiment 1:

[0026] Reference Figure 1 to Figure 4 , which is the first embodiment of the present invention, provides a maternal and child health data management system based on the whole life cycle, including the following steps: S1. Collect and standardize maternal and child health data to obtain health data.

[0027] In this embodiment, the maternal and child health data collection and standardization includes: Through electronic health records (EHR) (existing technology), maternal and child health data throughout the life cycle are collected, including pre-pregnancy examination data, pregnancy monitoring data, delivery and postpartum data, newborn health data, infant physical examination data, etc., and maternal and child physiological data, environmental data and psychosocial data are collected to ensure that the health data of women and children are collected comprehensively and systematically, and to ensure the consistency and standardization of the data to ensure that the data at different stages are comparable and integrable.

[0028] S2. Conduct maternal and child health risk testing based on health data to obtain health risk data.

[0029] In this embodiment, the maternal and child health risk detection includes: By using health risk detection models and combining maternal and child health data obtained from multiple sources, we can identify health risks, provide accurate health risk detection, and help women and children identify hidden risks in advance.

[0030] Furthermore, the health risk detection model includes: ; in: : Risk prediction value, indicating the degree of health risk that may be faced; : Neural network activation function, usually using Sigmoid nonlinear activation function to handle complex nonlinear relationships; : The weight parameter of the i-th category of physiological data, indicating the risk impact weight associated with physiological health data; : The i-th type of physiological data at time point t, including blood sugar, blood pressure, pregnancy weight, fetal heart rate, etc.; : The weight parameter of the jth type of environmental data, indicating the risk impact weight related to the external environment; : The jth type of environmental data at time point t, including air pollution index, environmental conditions, etc.; : Bias term, which represents the basic adjustment term of the model; This model combines different types of data, including physiological data and environmental data, and performs health risk detection through nonlinear feature extraction.

[0031] The steps to use this model include: Data input: In step S1, the collected maternal and child health data will be used as the input data of the model, including physiological data collected at different time points , Environmental data , and determine the impact of each data on the overall health risk, weight parameter , Automatically learn and adjust during training; Nonlinear mapping: through nonlinear activation functions in neural networks , capturing complex nonlinear relationships. For example, changes in certain physiological data (such as the relationship between maternal weight gain and gestational diabetes) may not be linear, but may be affected by other factors (such as lifestyle habits, genetic factors, etc.); Risk output: The model calculates a risk prediction value by weighting physiological and environmental data , indicating the degree of health risk that women and children may face at a certain point in time or stage. The values ​​of the risk factors are used to divide individuals into high-risk, medium-risk and low-risk groups, thereby providing data support for subsequent health interventions.

[0032] S3. Conduct health warning based on health risk data and obtain warning information.

[0033] In this embodiment, the health warning includes: According to health risk data, based on clinical experience and medical research data, high-risk factors are identified for maternal and child health data, and corresponding health warnings are provided to pregnant women, mothers, guardians of children and medical institutions. For example, the risk of gestational diabetes in pregnant women is identified, and relevant medical staff and patients are notified in advance, and more frequent examinations are recommended.

[0034] S4. Formulate intervention measures based on the early warning data and obtain intervention measures.

[0035] In this embodiment, the intervention measures formulation includes: After receiving the early warning information, the clinical decision support system CDSS (existing technology) is used in combination with health risk data to provide doctors or health managers with targeted treatment plans, monitoring recommendations and health management plans. For example, if the data shows that a pregnant woman is at risk of high blood pressure, the doctor will be prompted to conduct blood pressure monitoring and recommend changes in diet and lifestyle, or to start drug treatment. CDSS generates personalized intervention measures by combining patient health history, diagnostic data and expert experience, thereby ensuring that patients receive timely and effective intervention when facing health risks.

[0036] S5. Track the effects of intervention measures and obtain intervention results.

[0037] In this embodiment, the effect tracking includes: After implementing the intervention measures, the intervention effects are continuously tracked, and the health status of patients is monitored in real time, especially the physical signs and health data of pregnant women, parturients and infants, including blood sugar levels, blood pressure, weight changes, and fetal development. Through the monitored health status data, medical staff can promptly judge the intervention effects and adjust the intervention plan according to the patient's physical response. By tracking the intervention effects, changes in health trends can be identified in a timely manner, helping doctors make more effective treatment decisions.

[0038] S6. Conduct health assessment based on the intervention results and obtain the assessment results.

[0039] In this embodiment, the health assessment includes: Based on the intervention results, a health assessment model is used, combined with in-depth analysis of multidimensional data, including physiological data, environmental data, psychological data, and social data, to comprehensively and accurately evaluate the overall effect of the intervention measures on maternal and child health.

[0040] Furthermore, the health assessment model includes: ; in: : The health status assessment value at time point t, which represents the comprehensive effect of intervention measures on maternal and child health; , , : Weight coefficients of physiological data, environmental data, and psychosocial data, which are used to adjust the contribution of different data dimensions in health effect evaluation; : The change in the physiological data of the i-th category, indicating the change in the data before and after the health intervention, including the increase in the weight of pregnant women, the decrease in blood sugar levels, etc.; : The change in the jth type of environmental data indicates the changes in environmental factors before and after health intervention, including the decrease in air pollution index and the improvement of dietary structure; : The weight coefficient of the kth category of psychosocial data, indicating the impact of psychological state and social support on health outcomes; : The change in the kth category of psychosocial data indicates changes in mental health or social support levels, including a decrease in psychological stress and an increase in social support levels; The model formulation takes into account data from different dimensions and introduces dynamic weight adjustment to adjust the assessment strategy in real time during the intervention process.

[0041] Uses of the above models include: Data acquisition: data obtained in step S5 before and after the implementation of the intervention measures, including physiological data (such as gestational weight, fetal heart rate, blood sugar level, etc.), environmental data (such as air quality, eating habits, etc.), and psychosocial data (such as psychological stress, family support, etc.); Change calculation: Calculate the change of data based on the acquired data, including the change of physiological data , the change in environmental data , changes in psychosocial data , quantify the impact of health interventions on each dimension; Health assessment: Use the calculated change amount combined with the weight coefficient to obtain the relative importance of different data dimensions to the health assessment results, and calculate the health status assessment value , which represents the comprehensive effect of intervention measures on maternal and child health. A high value indicates that the health intervention has achieved good results; If the value is low, it means that the intervention effect is not obvious and the intervention measures may need to be adjusted.

[0042] S7. Perform long-term health prediction based on the evaluation results to obtain health prediction results.

[0043] In this embodiment, the long-term health prediction includes: Based on the health assessment results, a health prediction model is used, combined with historical data and individualized health records, and taking into account the long-term impact of environmental, psychological, and social factors, to predict the individual's future health status and more accurately infer the long-term trend of health status.

[0044] Furthermore, the health prediction model includes: ; in: : The long-term health prediction value at time point t+1, indicating the future health status; : Current health value, obtained through historical data; : Feedback coefficient, which is used to adjust the influence of health status assessment value on long-term health trend and is obtained through training model; : The feedback coefficient of the i-th type of physiological data, indicating the impact of changes in physiological data on long-term health; : The feedback coefficient of the jth type of environmental data, indicating the impact of changes in environmental factors on long-term health; : The feedback coefficient of the kth type of psychosocial data, indicating the impact of changes in psychosocial factors on long-term health; The model simulates the impact of multiple factors interacting on long-term health by taking into account the nonlinear feedback effects of the data.

[0045] The steps for using the steps include: Input data: Get the current health value by obtaining historical data , according to step S6, obtain the health status assessment value , and combined with the changes in the physiological, environmental, and psychosocial data obtained in step S6, the data are input into the model; Adjust the feedback coefficient: Dynamically adjust the feedback coefficient of different data according to the data of different dimensions, including , , , , used to determine the extent to which different data will affect future health trends; Predict health status: Based on the input data and the dynamically adjusted feedback coefficient, long-term health prediction is performed to obtain the long-term health prediction value. By comparing the predicted value with the minimum standard value and the maximum standard value of the health status, it is determined whether it is in a healthy state. For example, if it is between the minimum standard value and the maximum standard value, it is considered to be in a healthy state.

[0046] S8. Conduct personalized health management based on health prediction results.

[0047] In this embodiment, the personalized health management includes: Based on different health prediction results, personalized health management plans are developed for women and children, including health check-up plans, personalized diet and exercise recommendations, and mental health counseling to ensure that the health management plans are targeted and effective. The content and cycle of the health management plans are flexibly adjusted according to the prediction results to ensure the best health maintenance effect.

[0048] Embodiment 2: Reference Figure 5 , which is the second embodiment of the present invention, and provides a maternal and child health data management system based on the whole life cycle, including the following functional modules: Data processing module: Integrate multi-source data (such as physiological, environmental, psychosocial data, etc.), and perform data cleaning, standardization, and feature extraction to provide high-quality data input for subsequent analysis; Risk detection module: conduct health risk detection and identify potential health problems; Health warning module: Identify high-risk factors and issue warnings based on risk detection results; Health intervention module: used to generate individualized health intervention plans; Data monitoring module: continuously collect and analyze individual health data and dynamically adjust health intervention plans; Health assessment module: calculate the change in health status after intervention and evaluate the degree of improvement; Health prediction module: combines historical data to predict future health status; Health management module: Generates personalized health management plans based on health prediction results.

[0049] Embodiment three: This embodiment also provides a computer device, which is suitable for a maternal and child health data management system based on the entire life cycle, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a maternal and child health data management system based on the entire life cycle as proposed in the above embodiment.

[0050] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a maternal and child health data management system based on the entire life cycle as proposed in the above embodiment is implemented.

[0051] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0052] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0054] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0055] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A maternal and child health data management system based on the whole life cycle, characterized in that: The following steps are involved: S1. Collection and standardization of maternal and child health data to obtain health data; S2. Perform maternal and child health risk detection based on health data to obtain health risk data; S3. Conduct health warning based on health risk data and obtain warning information; S4, formulate intervention measures based on the early warning data and obtain intervention measures; S5. Follow up the effects of intervention measures and obtain the intervention results; S6. Conduct health assessment based on the intervention results and obtain the assessment results; S7. Perform long-term health prediction based on the evaluation results to obtain health prediction results; S8. Conduct personalized health management based on health prediction results.

2. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: The maternal and child health risk detection in S2 includes: By using health risk detection models and combining maternal and child health data obtained from multiple sources, we can identify health risks, provide accurate health risk detection, and help women and children identify hidden risks in advance.

3. A maternal and child health data management system based on the whole life cycle according to claim 2, characterized in that: The health risk detection model includes: ; in: : Risk prediction value; : Neural network activation function; : Weight parameter of the i-th category physiological data; : The i-th type of physiological data at time point t; : Weight parameter of the jth type of environmental data; : The j-th type of environmental data at time point t; : Bias term.

4. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: The health warnings in S3 include: Based on health risk data, clinical experience and medical research data, high-risk factors in maternal and child health data are identified, and health warnings are provided to maternal and child personnel and medical institutions.

5. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: The health assessment in S6 includes: Based on the intervention results, a health assessment model is used, combined with in-depth analysis of multidimensional data, to evaluate the overall effect of the intervention measures on maternal and child health.

6. A maternal and child health data management system based on the whole life cycle according to claim 5, characterized in that: The health assessment model includes: ; in: : Health status assessment value at time point t; , , : Weight coefficients of physiological data, environmental data and psychosocial data; : The change of the physiological data of the i-th category; : The change in the jth type of environmental data; : weight coefficient of the kth category of psychosocial data; : The amount of change in the kth category of psychosocial data.

7. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: The long-term health predictions in S7 include: Based on the health assessment results, a health prediction model is used to combine historical data and individualized health records, and consider the long-term impact of multiple factors to predict the individual's future health status.

8. A maternal and child health data management system based on the whole life cycle according to claim 7, characterized in that: The health prediction model includes: ; in: : The long-term health prediction value at time point t+1; : Current health value; : feedback coefficient; : Feedback coefficient of the i-th type of physiological data; : Feedback coefficient of the jth type of environmental data; : Feedback coefficient for the kth category of psychosocial data.

9. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: The personalized health management in S8 includes: Based on different health prediction results, personalized health management plans are formulated for women and children, and the content and cycle of the health management plans are flexibly adjusted according to the prediction results.

10. A maternal and child health data management system based on the whole life cycle according to claim 1, characterized in that: Includes the following functional modules: Data processing module, risk detection module, health warning module, health intervention module, data monitoring module, health assessment module, health prediction module, and health management module.

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